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library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6937
- Answer: {'precision': 0.6911447084233261, 'recall': 0.7911001236093943, 'f1': 0.7377521613832853, 'number': 809}
- Header: {'precision': 0.2653061224489796, 'recall': 0.3277310924369748, 'f1': 0.29323308270676696, 'number': 119}
- Question: {'precision': 0.7818343722172751, 'recall': 0.8244131455399061, 'f1': 0.8025594149908593, 'number': 1065}
- Overall Precision: 0.7090
- Overall Recall: 0.7812
- Overall F1: 0.7434
- Overall Accuracy: 0.8085
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7582 | 1.0 | 10 | 1.5548 | {'precision': 0.027379400260756193, 'recall': 0.02595797280593325, 'f1': 0.0266497461928934, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.29232995658465993, 'recall': 0.18967136150234742, 'f1': 0.23006833712984054, 'number': 1065} | 0.1529 | 0.1119 | 0.1292 | 0.3743 |
| 1.4081 | 2.0 | 20 | 1.1899 | {'precision': 0.21573604060913706, 'recall': 0.21013597033374537, 'f1': 0.2128991859737007, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5147410358565737, 'recall': 0.6065727699530516, 'f1': 0.5568965517241379, 'number': 1065} | 0.3994 | 0.4094 | 0.4044 | 0.6099 |
| 1.0527 | 3.0 | 30 | 0.8851 | {'precision': 0.5023419203747073, 'recall': 0.5302843016069221, 'f1': 0.5159350571256766, 'number': 809} | {'precision': 0.02564102564102564, 'recall': 0.008403361344537815, 'f1': 0.012658227848101267, 'number': 119} | {'precision': 0.610410094637224, 'recall': 0.7267605633802817, 'f1': 0.6635233604800685, 'number': 1065} | 0.5571 | 0.6041 | 0.5797 | 0.7406 |
| 0.7951 | 4.0 | 40 | 0.7518 | {'precision': 0.619914346895075, 'recall': 0.715698393077874, 'f1': 0.6643717728055079, 'number': 809} | {'precision': 0.17142857142857143, 'recall': 0.10084033613445378, 'f1': 0.12698412698412698, 'number': 119} | {'precision': 0.6454901960784314, 'recall': 0.7727699530516432, 'f1': 0.7034188034188034, 'number': 1065} | 0.6204 | 0.7095 | 0.6620 | 0.7752 |
| 0.6448 | 5.0 | 50 | 0.7019 | {'precision': 0.6666666666666666, 'recall': 0.7292954264524104, 'f1': 0.6965761511216056, 'number': 809} | {'precision': 0.25842696629213485, 'recall': 0.19327731092436976, 'f1': 0.22115384615384615, 'number': 119} | {'precision': 0.6956521739130435, 'recall': 0.8112676056338028, 'f1': 0.7490247074122236, 'number': 1065} | 0.6665 | 0.7411 | 0.7018 | 0.7875 |
| 0.5557 | 6.0 | 60 | 0.6785 | {'precision': 0.6462793068297655, 'recall': 0.7836835599505563, 'f1': 0.7083798882681563, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.2184873949579832, 'f1': 0.2639593908629441, 'number': 119} | {'precision': 0.7567324955116697, 'recall': 0.7915492957746478, 'f1': 0.7737494263423588, 'number': 1065} | 0.6917 | 0.7541 | 0.7216 | 0.8008 |
| 0.4907 | 7.0 | 70 | 0.6526 | {'precision': 0.6879659211927582, 'recall': 0.7985166872682324, 'f1': 0.7391304347826088, 'number': 809} | {'precision': 0.29245283018867924, 'recall': 0.2605042016806723, 'f1': 0.27555555555555555, 'number': 119} | {'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065} | 0.7104 | 0.7817 | 0.7444 | 0.8060 |
| 0.4323 | 8.0 | 80 | 0.6535 | {'precision': 0.6698113207547169, 'recall': 0.7898640296662547, 'f1': 0.7249007373794668, 'number': 809} | {'precision': 0.2542372881355932, 'recall': 0.25210084033613445, 'f1': 0.25316455696202533, 'number': 119} | {'precision': 0.7631806395851339, 'recall': 0.8291079812206573, 'f1': 0.7947794779477948, 'number': 1065} | 0.6963 | 0.7787 | 0.7352 | 0.8080 |
| 0.3857 | 9.0 | 90 | 0.6589 | {'precision': 0.6790648246546227, 'recall': 0.7898640296662547, 'f1': 0.7302857142857142, 'number': 809} | {'precision': 0.24427480916030533, 'recall': 0.2689075630252101, 'f1': 0.256, 'number': 119} | {'precision': 0.7683566433566433, 'recall': 0.8253521126760563, 'f1': 0.7958352195563604, 'number': 1065} | 0.6995 | 0.7777 | 0.7365 | 0.8069 |
| 0.3669 | 10.0 | 100 | 0.6754 | {'precision': 0.6898803046789989, 'recall': 0.7836835599505563, 'f1': 0.7337962962962964, 'number': 809} | {'precision': 0.2413793103448276, 'recall': 0.29411764705882354, 'f1': 0.26515151515151514, 'number': 119} | {'precision': 0.7688219663418955, 'recall': 0.8150234741784037, 'f1': 0.7912488605287148, 'number': 1065} | 0.7009 | 0.7712 | 0.7344 | 0.8078 |
| 0.3173 | 11.0 | 110 | 0.6832 | {'precision': 0.6913319238900634, 'recall': 0.8084054388133498, 'f1': 0.7452991452991453, 'number': 809} | {'precision': 0.26618705035971224, 'recall': 0.31092436974789917, 'f1': 0.2868217054263566, 'number': 119} | {'precision': 0.7753496503496503, 'recall': 0.8328638497652582, 'f1': 0.8030783159800814, 'number': 1065} | 0.7079 | 0.7918 | 0.7475 | 0.8040 |
| 0.2996 | 12.0 | 120 | 0.6900 | {'precision': 0.6995614035087719, 'recall': 0.788627935723115, 'f1': 0.7414294015107497, 'number': 809} | {'precision': 0.2605633802816901, 'recall': 0.31092436974789917, 'f1': 0.2835249042145594, 'number': 119} | {'precision': 0.7830357142857143, 'recall': 0.8234741784037559, 'f1': 0.802745995423341, 'number': 1065} | 0.7139 | 0.7787 | 0.7449 | 0.8066 |
| 0.2928 | 13.0 | 130 | 0.6954 | {'precision': 0.6995661605206074, 'recall': 0.7972805933250927, 'f1': 0.7452339688041594, 'number': 809} | {'precision': 0.2534246575342466, 'recall': 0.31092436974789917, 'f1': 0.2792452830188679, 'number': 119} | {'precision': 0.7707786526684165, 'recall': 0.8272300469483568, 'f1': 0.7980072463768116, 'number': 1065} | 0.7069 | 0.7842 | 0.7436 | 0.8033 |
| 0.2658 | 14.0 | 140 | 0.6914 | {'precision': 0.6937229437229437, 'recall': 0.792336217552534, 'f1': 0.7397576457010964, 'number': 809} | {'precision': 0.273972602739726, 'recall': 0.33613445378151263, 'f1': 0.3018867924528302, 'number': 119} | {'precision': 0.7839285714285714, 'recall': 0.8244131455399061, 'f1': 0.8036613272311213, 'number': 1065} | 0.7119 | 0.7822 | 0.7454 | 0.8091 |
| 0.2673 | 15.0 | 150 | 0.6937 | {'precision': 0.6911447084233261, 'recall': 0.7911001236093943, 'f1': 0.7377521613832853, 'number': 809} | {'precision': 0.2653061224489796, 'recall': 0.3277310924369748, 'f1': 0.29323308270676696, 'number': 119} | {'precision': 0.7818343722172751, 'recall': 0.8244131455399061, 'f1': 0.8025594149908593, 'number': 1065} | 0.7090 | 0.7812 | 0.7434 | 0.8085 |
### Framework versions
- Transformers 4.57.0
- Pytorch 2.9.1+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1
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